journal.pmed.0020124

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PLoS Medicine | www.plosmedicine.org 0696 Essay Open access, freely available online August 2005 | Volume 2 | Issue 8 | e124 P ublished research f ndings are sometimes reFuted by subsequent evidence, with ensuing conFusion and disappointment. ReFutation and controversy is seen across the range oF research designs, From clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5]. There is increasing concern that in modern research, False f ndings may be the majority or even the vast majority oF published research claims [6–8]. However, this should not be surprising. It can be proven that most claimed research f ndings are False. Here I will examine the key Factors that infl uence this problem and some corollaries thereoF. Modeling the Framework for False Positive Findings Several methodologists have pointed out [9–11] that the high rate oF nonreplication (lack oF conf rmation) oF research discoveries is a consequence oF the convenient, yet ill-Founded strategy oF claiming conclusive research f ndings solely on the basis oF a single study assessed by Formal statistical signif cance, typically For a p -value less than 0.05. Research is not most appropriately represented and summarized by p -values, but, unFortunately, there is a widespread notion that medical research articles should be interpreted based only on p -values. Research f ndings are def ned here as any relationship reaching Formal statistical signif cance, e.g., eFFective interventions, inFormative predictors, risk Factors, or associations. “Negative” research is also very useFul. “Negative” is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null f ndings. As has been shown previously, the probability that a research f nding is indeed true depends on the prior probability oF it being true (beFore doing the study), the statistical power oF the study, and the level oF statistical signif cance [10,11]. Consider a 2 × 2 table in which research f ndings are compared against the gold standard oF true relationships in a scientif c f eld. In a research f eld both true and False hypotheses can be made about the presence oF relationships. Let R be the ratio oF the number oF “true relationships” to “no relationships” among those tested in the f eld. R is characteristic oF the f eld and can vary a lot depending on whether the f eld targets highly likely relationships or searches For only one or a Few true relationships among thousands and millions oF hypotheses that may be postulated. Let us also consider, For computational simplicity, circumscribed f elds where either there is only one true relationship (among many that can be hypothesized) or the power is similar to f nd any oF the several existing true relationships. The pre-study probability oF a relationship being true is R ⁄( R + 1). The probability oF a study f nding a true relationship refl ects the power 1 − β (one minus the Type II error rate). The probability
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journal.pmed.0020124 - Open access, freely available online...

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